OpenRaiser/Intern-Atlas huggingface dataset:
DNA-based chemical compiler
Venue: arXiv cs.ET 2018
Year: 2018

Marcello, in 1997, formally proved that chemical kinetics can make a universal computer i.e they can replicate any digital circuit. Recently, Soloveichik et al. showed that chemical kinetics can perform fast and reliable Turing Universal computations. To simulate the behavior of chemical reactions, Sean et al. developed a software called CAIN which represents chemical reactions in XML format. In this work, we have made an attempt to create trans-compiler which can take python like code as input and gives CAIN supported chemical reactions file as output. This can be compared to generating assembly code from a high-level programming language. Additionally, Soloveichik et al. also showed DNA as a universal primer for implementing CRN's and Andrews Phillips developed Visual DSD programming language for simulating all the possible DSD reactions. CRN2DSD, a software developed by Manish Gupta's team, can, already, convert a CAIN file to Microsoft's Visual DSD code i.e assembly level to machine level. Hence, our attempt to convert high-level code to assembly code takes us one step closer to completing the dream of making a chemical compiler.
Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms
Venue: arXiv cs.AI 2018
Year: 2018

We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations.
Testing Graph Clusterability: Algorithms and Lower Bounds
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem of testing graph cluster structure: given access to a graph $G=(V, E)$, can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generalization of the well-studied problem of testing graph expansion, where one wants to distinguish between the graph having good expansion (i.e.\ being a good single cluster) and the graph having a sparse cut (i.e.\ being a union of at least two clusters). A recent work of Czumaj, Peng, and Sohler (STOC'15) gave an ingenious sublinear time algorithm for testing $k$-clusterability in time $\tilde{O}(n^{1/2} \text{poly}(k))$: their algorithm implicitly embeds a random sample of vertices of the graph into Euclidean space, and then clusters the samples based on estimates of Euclidean distances between the points. This yields a very efficient testing algorithm, but only works if the cluster structure is very strong: it is necessary to assume that the gap between conductances of accepted and rejected graphs is at least logarithmic in the size of the graph $G$. In this paper we show how one can leverage more refined geometric information, namely angles as opposed to distances, to obtain a sublinear time tester that works even when the gap is a sufficiently large constant. Our tester is based on the singular value decomposition of a natural matrix derived from random walk transition probabilities from a small sample of seed nodes. We complement our algorithm with a matching lower bound on the query complexity of testing clusterability. Our lower bound is based on a novel property testing problem, which we analyze using Fourier analytic tools. As a byproduct of our techniques, we also achieve new lower bounds for the problem of approximating MAX-CUT value in sublinear time.
An Experimental Study of Algorithms for Online Bipartite Matching
Venue: arXiv cs.DS 2018
Year: 2018

We perform an experimental study of algorithms for online bipartite matching under the known i.i.d. input model with integral types. In the last decade, there has been substantial effort in designing complex algorithms with the goal of improving worst-case approximation ratios. Our goal is to determine how these algorithms perform on more practical instances rather than worst-case instances. In particular, we are interested in whether the ranking of the algorithms by their worst-case performance is consistent with the ranking of the algorithms by their average-case/practical performance. We are also interested in whether preprocessing times and implementation difficulties that are introduced by these algorithms are justified in practice. To that end we evaluate these algorithms on different random inputs as well as real-life instances obtained from publicly available repositories. We compare these algorithms against several simple greedy-style algorithms. Most of the complex algorithms in the literature are presented as being non-greedy (i.e., an algorithm can intentionally skip matching a node that has available neighbors) to simplify the analysis. Every such algorithm can be turned into a greedy one without hurting its worst-case performance. On our benchmarks, non-greedy versions of these algorithms perform much worse than their greedy versions. Greedy versions perform about as well as the simplest greedy algorithm by itself. This, together with our other findings, suggests that simplest greedy algorithms are competitive with the state-of-the-art worst-case algorithms for online bipartite matching on many average-case and practical input families. Greediness is by far the most important property of online algorithms for bipartite matching.
Scale-Out Processors & Energy Efficiency
Venue: arXiv cs.AR 2018
Year: 2018

Scale-out workloads like media streaming or Web search serve millions of users and operate on a massive amount of data, and hence, require enormous computational power. As the number of users is increasing and the size of data is expanding, even more computational power is necessary for powering up such workloads. Data centers with thousands of servers are providing the computational power necessary for executing scale-out workloads. As operating data centers requires enormous capital outlay, it is important to optimize them to execute scale-out workloads efficiently. Server processors contribute significantly to the data center capital outlay, and hence, are a prime candidate for optimizations. While data centers are constrained with power, and power consumption is one of the major components contributing to the total cost of ownership (TCO), a recently-introduced scale-out design methodology optimizes server processors for data centers using performance per unit area. In this work, we use a more relevant performance-per-power metric as the optimization criterion for optimizing server processors and reevaluate the scale-out design methodology. Interestingly, we show that a scale-out processor that delivers the maximum performance per unit area, also delivers the highest performance per unit power.
Generalization of Equilibrium Propagation to Vector Field Dynamics
Venue: arXiv cs.LG 2018
Year: 2018

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections. We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues. In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism. Our learning method generalizes Equilibrium Propagation to vector field dynamics, relaxing the requirement of an energy function. As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function, but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights. We show experimentally that our algorithm optimizes the objective function.
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Within this thesis we propose a platform for combining Augmented Reality (AR) hardware with machine learning in a user-oriented pipeline, offering to the medical staff an intuitive 3D visualization of volumetric Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) medical image segmentations inside the AR headset, that does not need human intervention for loading, processing and segmentation of medical images. The AR visualization, based on Microsoft HoloLens, employs a modular and thus scalable frontend-backend architecture for real-time visualizations on multiple AR headsets. As Convolutional Neural Networks (CNNs) have lastly demonstrated superior performance for the machine learning task of image semantic segmentation, the pipeline also includes a fully automated CNN algorithm for the segmentation of the liver from CT scans. The model is based on the Deep Retinal Image Understanding (DRIU) model which is a Fully Convolutional Network with side outputs from feature maps with different resolution, extracted at different stages of the network. The algorithm is 2.5D which means that the input is a set of consecutive scan slices. The experiments have been performed on the Liver Tumor Segmentation Challenge (LiTS) dataset for liver segmentation and demonstrated good results and flexibility. While multiple approaches exist in the domain, only few of them have focused on overcoming the practical aspects which still largely hold this technology away from the operating rooms. In line with this, we also are next planning an evaluation from medical doctors and radiologists in a real-world environment.
Neural Material: Learning Elastic Constitutive Material and Damping Models from Sparse Data
Venue: arXiv cs.GR 2018
Year: 2018

The accuracy and fidelity of deformation simulations are highly dependent upon the underlying constitutive material model. Commonly used linear or nonlinear constitutive material models only cover a tiny part of possible material behavior. In this work we propose a unified framework for modeling deformable material. The key idea is to use a neural network to correct a nominal model of the elastic and damping properties of the object. The neural network encapsulates a complex function that is hard to explicitly model. It injects force corrections that help the forward simulation to more accurately predict the true behavior of a given soft object, which includes non-linear elastic forces and damping. Attempting to satisfy the requirement from real material interference and animation design scenarios, we learn material models from examples of dynamic behavior of a deformable object's surface. The challenge is that such data is sparse as it is consistently given only on part of the surface. Sparse reduced space-time optimization is employed to gradually generate increasingly accurate training data, which further refines and enhances the neural network. We evaluate our choice of network architecture and show evidence that the modest amount of training data we use is suitable for the problem tackled. Our method is demonstrated with a set of synthetic examples.
Automatic Derivation Of Formulas Using Reforcement Learning
Venue: arXiv cs.AI 2018
Year: 2018

This paper presents an artificial intelligence algorithm that can be used to derive formulas from various scientific disciplines called automatic derivation machine. First, the formula is abstractly expressed as a multiway tree model, and then each step of the formula derivation transformation is abstracted as a mapping of multiway trees. Derivation steps similar can be expressed as a reusable formula template by a multiway tree map. After that, the formula multiway tree is eigen-encoded to feature vectors construct the feature space of formulas, the Q-learning model using in this feature space can achieve the derivation by making training data from derivation process. Finally, an automatic formula derivation machine is made to choose the next derivation step based on the current state and object. We also make an example about the nuclear reactor physics problem to show how the automatic derivation machine works.
FlyNetSim: An Open Source Synchronized UAV Network Simulator based on ns-3 and Ardupilot
Venue: arXiv cs.NI 2018
Year: 2018

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to interconnect the UAVs with each other or with ground resources. Focusing either on the modeling of UAV operations or communication and network dynamics, available simulation tools fail to capture the complex interdependencies between these two aspects of the problem. The main contribution of this paper is a flexible and scalable open source simulator -- FlyNetSim -- bridging the two domains. The overall objective is to enable simulation and evaluation of UAV swarms operating within articulated multi-layered technological ecosystems, such as the Urban Internet of Things (IoT). To this aim, FlyNetSim interfaces two open source tools, ArduPilot and ns-3, creating individual data paths between the devices operating in the system using a publish and subscribe-based middleware. The capabilities of FlyNetSim are illustrated through several case-study scenarios including UAVs interconnecting with a multi-technology communication infrastructure and intra-swarm ad-hoc communications.
A Comparative Study of Bug Algorithms for Robot Navigation
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory -- relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO) methods. We compare a selection of Bug Algorithms in a simulated robot and environment where they endure different types noise and failure-cases of their on-board sensors. From the simulation results, we conclude that the implemented Bug Algorithms' performances are sensitive to many types of sensor-noise, which was most noticeable for odometry-drift. This raises the question if Bug Algorithms are suitable for real-world, on-board, robotic navigation as is. Variations that use multiple sensors to keep track of their progress towards the goal, were more adept in completing their task in the presence of sensor-failures. This shows that Bug Algorithms must spread their risk, by relying on the readings of multiple sensors, to be suitable for real-world deployment.
libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms
Venue: arXiv cs.DC 2018
Year: 2018

Hardware accelerators such as Graphics Processing Units (GPUs), Intel Xeon Phi co-processors (PHIs), and Field-Programmable Gate Arrays (FPGAs) are now ubiquitous in extreme-scale high performance computing (HPC), cloud, and Big data platforms to facilitate execution of workloads that demand high energy efficiency. They present unique interfaces and programming models therefore posing several limitations, which must be addressed to facilitate execution of large workloads. There is no library providing a unifying interface that allows programmers to write reusable out-of-core implementations of their data-parallel kernels that can run efficiently on different mainstream accelerators such as GPUs, PHIs, and FPGAs. We address this shortage in this paper. We present a library called libhclooc, which provides a unifying interface facilitating out-of-core implementations for data parallel kernels on the three different mainstream accelerators (GPUs, Intel Xeon Phis, FPGAs). We implement out-of-core matrix-matrix multiplication (MMOOC) using the libhclooc API and demonstrate its superior performance over vendor implementations. We show that it suffers from a maximum overhead of 10%, 4%, and 8% (due to abstraction) compared to the state-of-the-art optimised implementations for Nvidia K40c GPU, Nvidia P100 PCIe GPU, and Intel Xeon Phi 3120P respectively. We also show that using libhclooc API reduces the number of lines of code (LOC) by 75% thereby drastically improving programmer productivity.
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification
Venue: arXiv cs.CV 2018
Year: 2018

In this report, we are presenting our automated prediction system for disease classification within dermoscopic images. The proposed solution is based on deep learning, where we employed transfer learning strategy on VGG16 and GoogLeNet architectures. The key feature of our solution is preprocessing based primarily on image augmentation and colour normalization. The solution was evaluated on Task 3: Lesion Diagnosis of the ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection.
A Spectrum Sharing Solution for the Efficient Use of mmWave Bands in 5G Cellular Scenarios
Venue: arXiv cs.NI 2018
Year: 2018

Regulators all around the world have started identifying the portions of the spectrum that will be used for the next generation of cellular networks. A band in the mmWave spectrum will be exploited to increase the available capacity. In response to the very high expected traffic demand, a sharing mechanism may make it possible to use the spectrum more efficiently. In this work, moving within the European and Italian regulatory conditions, we propose the use of Licensed Spectrum Access (LSA) to coordinate sharing among cellular operators. Additionally, we show some preliminary results on our research activities which are focused on a dynamic spectrum sharing approach applied in simulated 5G cellular scenarios.
LSTM-Based Goal Recognition in Latent Space
Venue: arXiv cs.AI 2018
Year: 2018

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.
Toward domain-invariant speech recognition via large scale training
Venue: arXiv cs.CL 2018
Year: 2018

Current state-of-the-art automatic speech recognition systems are trained to work in specific `domains', defined based on factors like application, sampling rate and codec. When such recognizers are used in conditions that do not match the training domain, performance significantly drops. This work explores the idea of building a single domain-invariant model for varied use-cases by combining large scale training data from multiple application domains. Our final system is trained using 162,000 hours of speech. Additionally, each utterance is artificially distorted during training to simulate effects like background noise, codec distortion, and sampling rates. Our results show that, even at such a scale, a model thus trained works almost as well as those fine-tuned to specific subsets: A single model can be robust to multiple application domains, and variations like codecs and noise. More importantly, such models generalize better to unseen conditions and allow for rapid adaptation -- we show that by using as little as 10 hours of data from a new domain, an adapted domain-invariant model can match performance of a domain-specific model trained from scratch using 70 times as much data. We also highlight some of the limitations of such models and areas that need addressing in future work.
Limitations of performance of Exascale Applications and supercomputers they are running on
Venue: arXiv cs.DC 2018
Year: 2018

The paper highlights that the cooperation of the components of the computing systems receives even more focus in the coming age of exascale computing. It discovers that inherent performance limitations exist and identifies the major critical contributions of the performance on many-many processor systems. The extended and reinterpreted simple Amdahl model describes the behavior of the existing supercomputers surprisingly well, and explains some mystical happenings around high-performance computing. It is pointed out that using the present technology and paradigm only marginal development of performance is possible, and that the major obstacle towards higher performance applications is the 70-years old computing paradigm itself. A way to step forward is also suggested
Formalisation of a frame stack semantics for a Java-like language
Venue: arXiv cs.LO 2018
Year: 2018

We present a Coq formalisation of the small-step operational semantics of Jafun, a small Java-like language with classes. This format of semantics makes it possible to naturally specify and prove invariants that should hold at each computation step. In contrast to the Featherweight Java approach the semantics explicitly manipulates frame stack of method calls. Thanks to that one can express properties of computation that depend on execution of particular methods. On the basis of the semantics, we developed a type system that makes it possible to delineate a notion of a compound value and classify certain methods as extensional functions operating on them. In our formalisation we make a mechanised proof that the operational semantics for the untyped version of the semantics agrees with the one for the typed one. We discuss different methods to make such formalisation effort and provide experiments that substantiate it.
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing
Venue: arXiv cs.SE 2018
Year: 2018

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.